{
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.13",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [],
   "dockerImageVersionId": 30699,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook",
   "isGpuEnabled": true
  }
 },
 "nbformat_minor": 4,
 "nbformat": 4,
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 进阶RAG检索   Contextual Compression + Filtering  "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 前期准备"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "! pip install langchain openai chromadb tiktoken"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:01:26.094421Z",
     "iopub.execute_input": "2024-05-09T21:01:26.095163Z",
     "iopub.status.idle": "2024-05-09T21:01:41.271480Z",
     "shell.execute_reply.started": "2024-05-09T21:01:26.095126Z",
     "shell.execute_reply": "2024-05-09T21:01:41.270286Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "trusted": true
   },
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "text": "Requirement already satisfied: langchain in /opt/conda/lib/python3.10/site-packages (0.1.19)\nRequirement already satisfied: openai in /opt/conda/lib/python3.10/site-packages (1.27.0)\nRequirement already satisfied: chromadb in /opt/conda/lib/python3.10/site-packages (0.5.0)\nCollecting tiktoken\n  Downloading tiktoken-0.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\nRequirement already satisfied: PyYAML>=5.3 in /opt/conda/lib/python3.10/site-packages (from langchain) (6.0.1)\nRequirement already satisfied: SQLAlchemy<3,>=1.4 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.0.25)\nRequirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /opt/conda/lib/python3.10/site-packages (from langchain) (3.9.1)\nRequirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /opt/conda/lib/python3.10/site-packages (from langchain) (4.0.3)\nRequirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /opt/conda/lib/python3.10/site-packages (from langchain) (0.6.4)\nRequirement already satisfied: langchain-community<0.1,>=0.0.38 in /opt/conda/lib/python3.10/site-packages (from langchain) (0.0.38)\nRequirement already satisfied: langchain-core<0.2.0,>=0.1.52 in /opt/conda/lib/python3.10/site-packages (from langchain) (0.1.52)\nRequirement already satisfied: langchain-text-splitters<0.1,>=0.0.1 in /opt/conda/lib/python3.10/site-packages (from langchain) (0.0.1)\nRequirement already satisfied: langsmith<0.2.0,>=0.1.17 in /opt/conda/lib/python3.10/site-packages (from langchain) (0.1.56)\nRequirement already satisfied: numpy<2,>=1 in /opt/conda/lib/python3.10/site-packages (from langchain) (1.26.4)\nRequirement already satisfied: pydantic<3,>=1 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.5.3)\nRequirement already satisfied: requests<3,>=2 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.31.0)\nRequirement already satisfied: tenacity<9.0.0,>=8.1.0 in /opt/conda/lib/python3.10/site-packages (from langchain) (8.2.3)\nRequirement already satisfied: anyio<5,>=3.5.0 in /opt/conda/lib/python3.10/site-packages (from openai) (4.2.0)\nRequirement already satisfied: distro<2,>=1.7.0 in /opt/conda/lib/python3.10/site-packages (from openai) (1.9.0)\nRequirement already satisfied: httpx<1,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from openai) (0.27.0)\nRequirement already satisfied: sniffio in /opt/conda/lib/python3.10/site-packages (from openai) (1.3.0)\nRequirement already satisfied: tqdm>4 in /opt/conda/lib/python3.10/site-packages (from openai) (4.66.1)\nRequirement already satisfied: typing-extensions<5,>=4.7 in /opt/conda/lib/python3.10/site-packages (from openai) (4.9.0)\nRequirement already satisfied: build>=1.0.3 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.2.1)\nRequirement already satisfied: chroma-hnswlib==0.7.3 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.7.3)\nRequirement already satisfied: fastapi>=0.95.2 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.108.0)\nRequirement already satisfied: uvicorn>=0.18.3 in /opt/conda/lib/python3.10/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.25.0)\nRequirement already satisfied: posthog>=2.4.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (3.5.0)\nRequirement already satisfied: onnxruntime>=1.14.1 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.17.3)\nRequirement already satisfied: opentelemetry-api>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.22.0)\nRequirement already satisfied: opentelemetry-exporter-otlp-proto-grpc>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.22.0)\nRequirement already satisfied: opentelemetry-instrumentation-fastapi>=0.41b0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.43b0)\nRequirement already satisfied: opentelemetry-sdk>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.22.0)\nRequirement already satisfied: tokenizers>=0.13.2 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.15.2)\nRequirement already satisfied: pypika>=0.48.9 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.48.9)\nRequirement already satisfied: overrides>=7.3.1 in /opt/conda/lib/python3.10/site-packages (from chromadb) (7.4.0)\nRequirement already satisfied: importlib-resources in /opt/conda/lib/python3.10/site-packages (from chromadb) (6.1.1)\nRequirement already satisfied: grpcio>=1.58.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.60.0)\nRequirement already satisfied: bcrypt>=4.0.1 in /opt/conda/lib/python3.10/site-packages (from chromadb) (4.1.3)\nRequirement already satisfied: typer>=0.9.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.9.0)\nRequirement already satisfied: kubernetes>=28.1.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (29.0.0)\nRequirement already satisfied: mmh3>=4.0.1 in /opt/conda/lib/python3.10/site-packages (from chromadb) (4.1.0)\nRequirement already satisfied: orjson>=3.9.12 in /opt/conda/lib/python3.10/site-packages (from chromadb) (3.10.3)\nRequirement already satisfied: regex>=2022.1.18 in /opt/conda/lib/python3.10/site-packages (from tiktoken) (2023.12.25)\nRequirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.2.0)\nRequirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.4)\nRequirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.3)\nRequirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.1)\nRequirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\nRequirement already satisfied: idna>=2.8 in /opt/conda/lib/python3.10/site-packages (from anyio<5,>=3.5.0->openai) (3.6)\nRequirement already satisfied: exceptiongroup>=1.0.2 in /opt/conda/lib/python3.10/site-packages (from anyio<5,>=3.5.0->openai) (1.2.0)\nRequirement already satisfied: packaging>=19.1 in /opt/conda/lib/python3.10/site-packages (from build>=1.0.3->chromadb) (23.2)\nRequirement already satisfied: pyproject_hooks in /opt/conda/lib/python3.10/site-packages (from build>=1.0.3->chromadb) (1.1.0)\nRequirement already satisfied: tomli>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from build>=1.0.3->chromadb) (2.0.1)\nRequirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /opt/conda/lib/python3.10/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (3.21.1)\nRequirement already satisfied: typing-inspect<1,>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (0.9.0)\nRequirement already satisfied: starlette<0.33.0,>=0.29.0 in /opt/conda/lib/python3.10/site-packages (from fastapi>=0.95.2->chromadb) (0.32.0.post1)\nRequirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\nRequirement already satisfied: httpcore==1.* in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\nRequirement already satisfied: h11<0.15,>=0.13 in /opt/conda/lib/python3.10/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\nRequirement already satisfied: six>=1.9.0 in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (1.16.0)\nRequirement already satisfied: python-dateutil>=2.5.3 in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (2.9.0.post0)\nRequirement already satisfied: google-auth>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (2.26.1)\nRequirement already satisfied: websocket-client!=0.40.0,!=0.41.*,!=0.42.*,>=0.32.0 in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (1.7.0)\nRequirement already satisfied: requests-oauthlib in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (1.3.1)\nRequirement already satisfied: oauthlib>=3.2.2 in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (3.2.2)\nRequirement already satisfied: urllib3>=1.24.2 in /opt/conda/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb) (1.26.18)\nRequirement already satisfied: jsonpatch<2.0,>=1.33 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.2.0,>=0.1.52->langchain) (1.33)\nRequirement already satisfied: coloredlogs in /opt/conda/lib/python3.10/site-packages (from onnxruntime>=1.14.1->chromadb) (15.0.1)\nRequirement already satisfied: flatbuffers in /opt/conda/lib/python3.10/site-packages (from onnxruntime>=1.14.1->chromadb) (23.5.26)\nRequirement already satisfied: protobuf in /opt/conda/lib/python3.10/site-packages (from onnxruntime>=1.14.1->chromadb) (3.20.3)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from onnxruntime>=1.14.1->chromadb) (1.12)\nRequirement already satisfied: deprecated>=1.2.6 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-api>=1.2.0->chromadb) (1.2.14)\nRequirement already satisfied: importlib-metadata<7.0,>=6.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-api>=1.2.0->chromadb) (6.11.0)\nRequirement already satisfied: backoff<3.0.0,>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb) (2.2.1)\nRequirement already satisfied: googleapis-common-protos~=1.52 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb) (1.62.0)\nRequirement already satisfied: opentelemetry-exporter-otlp-proto-common==1.22.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb) (1.22.0)\nRequirement already satisfied: opentelemetry-proto==1.22.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb) (1.22.0)\nRequirement already satisfied: opentelemetry-instrumentation-asgi==0.43b0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (0.43b0)\nRequirement already satisfied: opentelemetry-instrumentation==0.43b0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (0.43b0)\nRequirement already satisfied: opentelemetry-semantic-conventions==0.43b0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (0.43b0)\nRequirement already satisfied: opentelemetry-util-http==0.43b0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (0.43b0)\nRequirement already satisfied: setuptools>=16.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation==0.43b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (69.0.3)\nRequirement already satisfied: wrapt<2.0.0,>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation==0.43b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (1.14.1)\nRequirement already satisfied: asgiref~=3.0 in /opt/conda/lib/python3.10/site-packages (from opentelemetry-instrumentation-asgi==0.43b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (3.8.1)\nRequirement already satisfied: monotonic>=1.5 in /opt/conda/lib/python3.10/site-packages (from posthog>=2.4.0->chromadb) (1.6)\nRequirement already satisfied: annotated-types>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from pydantic<3,>=1->langchain) (0.6.0)\nRequirement already satisfied: pydantic-core==2.14.6 in /opt/conda/lib/python3.10/site-packages (from pydantic<3,>=1->langchain) 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fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers>=0.13.2->chromadb) (2024.2.0)\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.10/site-packages (from importlib-metadata<7.0,>=6.0->opentelemetry-api>=1.2.0->chromadb) (3.17.0)\nRequirement already satisfied: jsonpointer>=1.9 in /opt/conda/lib/python3.10/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.2.0,>=0.1.52->langchain) (2.4)\nRequirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\nRequirement already satisfied: humanfriendly>=9.1 in /opt/conda/lib/python3.10/site-packages (from coloredlogs->onnxruntime>=1.14.1->chromadb) (10.0)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->onnxruntime>=1.14.1->chromadb) (1.3.0)\nRequirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth>=1.0.1->kubernetes>=28.1.0->chromadb) (0.5.1)\nDownloading tiktoken-0.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n\u001B[2K   \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m1.8/1.8 MB\u001B[0m \u001B[31m7.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m00:01\u001B[0m00:01\u001B[0m\n\u001B[?25hInstalling collected packages: tiktoken\nSuccessfully installed tiktoken-0.6.0\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.document_loaders import PyPDFLoader\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores import Chroma\n",
    "\n",
    "\n",
    "# Load pdf\n",
    "loader = PyPDFLoader(\"https://arxiv.org/pdf/2309.10305v2.pdf\")\n",
    "data = loader.load()\n",
    "\n",
    "# Split\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
    "splits = text_splitter.split_documents(data[:6])\n",
    "\n",
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "OPENAI_API_KEY = getpass()\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
    "\n",
    "# VectorDB\n",
    "embedding = OpenAIEmbeddings()\n",
    "vectordb = Chroma.from_documents(documents=splits, embedding=embedding)\n",
    "\n",
    "retriever = vectordb.as_retriever()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:01:41.274018Z",
     "iopub.execute_input": "2024-05-09T21:01:41.274426Z",
     "iopub.status.idle": "2024-05-09T21:01:48.990183Z",
     "shell.execute_reply.started": "2024-05-09T21:01:41.274386Z",
     "shell.execute_reply": "2024-05-09T21:01:48.989366Z"
    },
    "trusted": true
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdin",
     "text": " ························································\n"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "base_docs = retriever.get_relevant_documents(\n",
    "    \"What is baichuan2 ？\"\n",
    ")\n",
    "\n",
    "base_docs"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:02:05.060844Z",
     "iopub.execute_input": "2024-05-09T21:02:05.061211Z",
     "iopub.status.idle": "2024-05-09T21:02:05.307496Z",
     "shell.execute_reply.started": "2024-05-09T21:02:05.061184Z",
     "shell.execute_reply": "2024-05-09T21:02:05.306560Z"
    },
    "trusted": true
   },
   "execution_count": 11,
   "outputs": [
    {
     "execution_count": 11,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[Document(page_content='In this technical report, we introduce Baichuan\\n2, a series of large-scale multilingual language\\nmodels. Baichuan 2 has two separate models,\\nBaichuan 2-7B with 7 billion parameters and\\nBaichuan 2-13B with 13 billion parameters. Both\\nmodels were trained on 2.6 trillion tokens, which\\nto our knowledge is the largest to date, more than\\ndouble that of Baichuan 1 (Baichuan, 2023b,a).\\nWith such a massive amount of training data,\\nBaichuan 2 achieves significant improvements over', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='demonstrates strong performance on medical and\\nlegal domain tasks. On benchmarks such as\\nMedQA (Jin et al., 2021) and JEC-QA (Zhong\\net al., 2020), Baichuan 2 outperforms other open-\\nsource models, making it a suitable foundation\\nmodel for domain-specific optimization.\\nAdditionally, we also released two chat\\nmodels, Baichuan 2-7B-Chat and Baichuan 2-\\n13B-Chat, optimized to follow human instructions.\\nThese models excel at dialogue and context\\nunderstanding. We will elaborate on our', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 1. On general benchmarks like MMLU\\n(Hendrycks et al., 2021a), CMMLU (Li et al.,\\n2023), and C-Eval (Huang et al., 2023), Baichuan\\n2-7B achieves nearly 30% higher performance\\ncompared to Baichuan 1-7B. Specifically, Baichuan\\n2 is optimized to improve performance on math\\nand code problems. On the GSM8K (Cobbe\\net al., 2021) and HumanEval (Chen et al., 2021)\\nevaluations, Baichuan 2 nearly doubles the results\\nof the Baichuan 1. In addition, Baichuan 2 also', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 1-13B-Base 52.40 51.60 55.30 49.69 43.20 43.01 26.76 11.5913B\\nBaichuan 2-13B-Base 58.10 59.17 61.97 54.33 48.17 48.78 52.77 17.07\\nTable 1: Overall results of Baichuan 2 compared with other similarly sized LLMs on general benchmarks. * denotes\\nresults derived from official websites.\\nFigure 1: The distribution of different categories of\\nBaichuan 2 training data.\\nData processing : For data processing, we focus\\non data frequency and quality. Data frequency', metadata={'page': 2, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'})]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Contextual Extractor"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "from langchain.retrievers import ContextualCompressionRetriever\n",
    "from langchain.retrievers.document_compressors import LLMChainExtractor\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "llm = ChatOpenAI(temperature=0)\n",
    "compressor = LLMChainExtractor.from_llm(llm)\n",
    "compression_retriever = ContextualCompressionRetriever(\n",
    "    base_compressor=compressor, base_retriever=retriever\n",
    ")\n",
    "\n",
    "compressed_docs = compression_retriever.get_relevant_documents(\n",
    "    \"What is baichuan2 ？\"\n",
    ")"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:05:08.029597Z",
     "iopub.execute_input": "2024-05-09T21:05:08.030512Z",
     "iopub.status.idle": "2024-05-09T21:05:15.640963Z",
     "shell.execute_reply.started": "2024-05-09T21:05:08.030481Z",
     "shell.execute_reply": "2024-05-09T21:05:15.640130Z"
    },
    "trusted": true
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "compressed_docs"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:05:19.365597Z",
     "iopub.execute_input": "2024-05-09T21:05:19.367184Z",
     "iopub.status.idle": "2024-05-09T21:05:19.373553Z",
     "shell.execute_reply.started": "2024-05-09T21:05:19.367145Z",
     "shell.execute_reply": "2024-05-09T21:05:19.372475Z"
    },
    "trusted": true
   },
   "execution_count": 15,
   "outputs": [
    {
     "execution_count": 15,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[Document(page_content='Baichuan 2, a series of large-scale multilingual language models. Baichuan 2 has two separate models,\\nBaichuan 2-7B with 7 billion parameters and\\nBaichuan 2-13B with 13 billion parameters.', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 2 outperforms other open-source models, making it a suitable foundation model for domain-specific optimization.', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 1. On general benchmarks like MMLU (Hendrycks et al., 2021a), CMMLU (Li et al., 2023), and C-Eval (Huang et al., 2023), Baichuan 2-7B achieves nearly 30% higher performance compared to Baichuan 1-7B. Specifically, Baichuan 2 is optimized to improve performance on math and code problems. On the GSM8K (Cobbe et al., 2021) and HumanEval (Chen et al., 2021) evaluations, Baichuan 2 nearly doubles the results of the Baichuan 1.', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 2-13B-Base 58.10 59.17 61.97 54.33 48.17 48.78 52.77 17.07', metadata={'page': 2, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'})]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Contextual filters"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### LLMChainFilter"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "from langchain.retrievers.document_compressors import LLMChainFilter\n",
    "\n",
    "_filter = LLMChainFilter.from_llm(llm)\n",
    "compression_retriever = ContextualCompressionRetriever(\n",
    "    base_compressor=_filter, base_retriever=retriever\n",
    ")\n",
    "\n",
    "compressed_docs = compression_retriever.get_relevant_documents(\n",
    "    \"What is baichuan2 ？\"\n",
    ")\n",
    "\n",
    "compressed_docs"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:06:03.323066Z",
     "iopub.execute_input": "2024-05-09T21:06:03.323452Z",
     "iopub.status.idle": "2024-05-09T21:06:08.129695Z",
     "shell.execute_reply.started": "2024-05-09T21:06:03.323423Z",
     "shell.execute_reply": "2024-05-09T21:06:08.128795Z"
    },
    "trusted": true
   },
   "execution_count": 16,
   "outputs": [
    {
     "execution_count": 16,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[Document(page_content='In this technical report, we introduce Baichuan\\n2, a series of large-scale multilingual language\\nmodels. Baichuan 2 has two separate models,\\nBaichuan 2-7B with 7 billion parameters and\\nBaichuan 2-13B with 13 billion parameters. Both\\nmodels were trained on 2.6 trillion tokens, which\\nto our knowledge is the largest to date, more than\\ndouble that of Baichuan 1 (Baichuan, 2023b,a).\\nWith such a massive amount of training data,\\nBaichuan 2 achieves significant improvements over', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='demonstrates strong performance on medical and\\nlegal domain tasks. On benchmarks such as\\nMedQA (Jin et al., 2021) and JEC-QA (Zhong\\net al., 2020), Baichuan 2 outperforms other open-\\nsource models, making it a suitable foundation\\nmodel for domain-specific optimization.\\nAdditionally, we also released two chat\\nmodels, Baichuan 2-7B-Chat and Baichuan 2-\\n13B-Chat, optimized to follow human instructions.\\nThese models excel at dialogue and context\\nunderstanding. We will elaborate on our', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 1. On general benchmarks like MMLU\\n(Hendrycks et al., 2021a), CMMLU (Li et al.,\\n2023), and C-Eval (Huang et al., 2023), Baichuan\\n2-7B achieves nearly 30% higher performance\\ncompared to Baichuan 1-7B. Specifically, Baichuan\\n2 is optimized to improve performance on math\\nand code problems. On the GSM8K (Cobbe\\net al., 2021) and HumanEval (Chen et al., 2021)\\nevaluations, Baichuan 2 nearly doubles the results\\nof the Baichuan 1. In addition, Baichuan 2 also', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}),\n Document(page_content='Baichuan 1-13B-Base 52.40 51.60 55.30 49.69 43.20 43.01 26.76 11.5913B\\nBaichuan 2-13B-Base 58.10 59.17 61.97 54.33 48.17 48.78 52.77 17.07\\nTable 1: Overall results of Baichuan 2 compared with other similarly sized LLMs on general benchmarks. * denotes\\nresults derived from official websites.\\nFigure 1: The distribution of different categories of\\nBaichuan 2 training data.\\nData processing : For data processing, we focus\\non data frequency and quality. Data frequency', metadata={'page': 2, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'})]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### EmbeddingsFilter"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
    "\n",
    "embeddings_filter = EmbeddingsFilter(embeddings=embedding, similarity_threshold=0.76)\n",
    "compression_retriever = ContextualCompressionRetriever(\n",
    "    base_compressor=embeddings_filter, base_retriever=retriever\n",
    ")\n",
    "\n",
    "compressed_docs = compression_retriever.get_relevant_documents(\n",
    "    \"What is baichuan2 ？\"\n",
    ")"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:08:11.542100Z",
     "iopub.execute_input": "2024-05-09T21:08:11.543040Z",
     "iopub.status.idle": "2024-05-09T21:08:12.175204Z",
     "shell.execute_reply.started": "2024-05-09T21:08:11.542999Z",
     "shell.execute_reply": "2024-05-09T21:08:12.174343Z"
    },
    "trusted": true
   },
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "compressed_docs"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-05-09T21:08:13.246516Z",
     "iopub.execute_input": "2024-05-09T21:08:13.247239Z",
     "iopub.status.idle": "2024-05-09T21:08:13.261530Z",
     "shell.execute_reply.started": "2024-05-09T21:08:13.247204Z",
     "shell.execute_reply": "2024-05-09T21:08:13.260473Z"
    },
    "trusted": true
   },
   "execution_count": 20,
   "outputs": [
    {
     "execution_count": 20,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[_DocumentWithState(page_content='In this technical report, we introduce Baichuan\\n2, a series of large-scale multilingual language\\nmodels. Baichuan 2 has two separate models,\\nBaichuan 2-7B with 7 billion parameters and\\nBaichuan 2-13B with 13 billion parameters. Both\\nmodels were trained on 2.6 trillion tokens, which\\nto our knowledge is the largest to date, more than\\ndouble that of Baichuan 1 (Baichuan, 2023b,a).\\nWith such a massive amount of training data,\\nBaichuan 2 achieves significant improvements over', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}, state={'embedded_doc': [-0.014723360590013786, 0.0002893662431127455, 0.011971203826286712, -0.0010278220838435682, 0.0057991874664249056, 0.01773310905529369, -0.027819831124549266, -0.003948599329826443, -0.012045770163767604, -0.03367663718691621, -0.0003272846611226113, 0.026965713379761894, -0.0192786555393206, -0.002374243547828515, 0.012676189827434818, 0.0022115545422806845, 0.010703585402336742, 0.02326453710656497, 0.005761904763345719, 0.014777590568970569, -0.004901008387604065, 0.011428906830686437, -0.016851876786689188, -0.002269173603883979, -0.018153388831071832, 0.028985768687201215, 0.049213443735991666, -0.023657702358525982, 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On benchmarks such as\\nMedQA (Jin et al., 2021) and JEC-QA (Zhong\\net al., 2020), Baichuan 2 outperforms other open-\\nsource models, making it a suitable foundation\\nmodel for domain-specific optimization.\\nAdditionally, we also released two chat\\nmodels, Baichuan 2-7B-Chat and Baichuan 2-\\n13B-Chat, optimized to follow human instructions.\\nThese models excel at dialogue and context\\nunderstanding. We will elaborate on our', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}, state={'embedded_doc': [0.005882351368818686, -0.005780810670505424, 0.028095230935712485, -0.0098039186375903, 0.006988793732141566, 0.017058818690177432, -0.01738094825167176, -0.0026050413603906693, -0.014187671475002462, -0.026050413603906693, -0.01329831620215298, 0.018851536326707063, -0.02168066692600267, -0.011939772746570146, -0.012682069398180604, 0.0023354335000680708, 0.010322126050295819, 0.007696076381965468, 0.010854338691186946, -0.004257702058451552, -0.015840332173063344, 0.011533609953317099, -0.008291313946675637, -0.005378149649960044, -0.018193271975532797, 0.01733893070446987, 0.039915954586092296, -0.031344527693801694, -0.04098038173051961, 0.0006285012040118118, 0.006032911762765378, -0.00675419964077489, -0.033053213961217666, -0.01963584959419688, -0.015728286622288347, -0.006088934072491612, -0.006330530312289829, 0.011246495324931854, 0.04204480514965681, 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On general benchmarks like MMLU\\n(Hendrycks et al., 2021a), CMMLU (Li et al.,\\n2023), and C-Eval (Huang et al., 2023), Baichuan\\n2-7B achieves nearly 30% higher performance\\ncompared to Baichuan 1-7B. Specifically, Baichuan\\n2 is optimized to improve performance on math\\nand code problems. On the GSM8K (Cobbe\\net al., 2021) and HumanEval (Chen et al., 2021)\\nevaluations, Baichuan 2 nearly doubles the results\\nof the Baichuan 1. In addition, Baichuan 2 also', metadata={'page': 1, 'source': 'https://arxiv.org/pdf/2309.10305v2.pdf'}, state={'embedded_doc': [-0.007791932106202458, 0.012492159220548317, 0.01680940554238904, -0.019260486339712155, 0.004101383446955148, 0.022477531516013234, -0.02619593184215684, -0.026349125090481457, -0.02459437368412954, -0.045623536565149836, 0.0009374341923096424, 0.030582811289359194, -0.0176171471888808, 0.0012272815061923686, 0.006872776807206289, 0.012227553716703136, 0.005758648664065292, 0.023661290921870737, 0.0024423773551448358, 0.00904532603440544, -0.02991433403094557, 0.00864841777863767, -0.017087936181190443, -0.02743539923841975, -0.019580797971317617, 0.022616795904091373, 0.04138985222891722, -0.020165715106768255, -0.020653146984299684, 0.0021916986160703676, 0.020555659863735348, -0.00140745691538576, -0.02771393173986628, -0.011593893952292893, -0.028298848875316918, -0.0117610132668963, -0.0042580575133575405, -0.002877583457332477, 0.03364666507998078, 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43.20 43.01 26.76 11.5913B\\nBaichuan 2-13B-Base 58.10 59.17 61.97 54.33 48.17 48.78 52.77 17.07\\nTable 1: Overall results of Baichuan 2 compared with other similarly sized LLMs on general benchmarks. * denotes\\nresults derived from official websites.\\nFigure 1: The distribution of different categories of\\nBaichuan 2 training data.\\nData processing : For data processing, we focus\\non data frequency and quality. 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